Weekly Exercises #4

library(tidyverse)     # for data cleaning and plotting

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## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

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library(googlesheets4) # for reading googlesheet data
library(lubridate)     # for date manipulation

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
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##     date, intersect, setdiff, union

library(openintro)     # for the abbr2state() function

## Loading required package: airports

## Loading required package: cherryblossom

## Loading required package: usdata

library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data

## 
## Attaching package: 'maps'

## The following object is masked from 'package:purrr':
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##     map

library(ggmap)         # for mapping points on maps

## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.

## Please cite ggmap if you use it! See citation("ggmap") for details.

library(gplots)        # for col2hex() function

## 
## Attaching package: 'gplots'

## The following object is masked from 'package:stats':
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##     lowess

library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data

## Linking to GEOS 3.8.1, GDAL 3.1.1, PROJ 6.3.1

library(leaflet)       # for highly customizable mapping
library(carData)       # for Minneapolis police stops data
library(ggthemes)      # for more themes (including theme_map())
gs4_deauth()           # To not have to authorize each time you knit.
theme_set(theme_minimal())

# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")

## Parsed with column specification:
## cols(
##   Brand = col_character(),
##   `Store Number` = col_character(),
##   `Store Name` = col_character(),
##   `Ownership Type` = col_character(),
##   `Street Address` = col_character(),
##   City = col_character(),
##   `State/Province` = col_character(),
##   Country = col_character(),
##   Postcode = col_character(),
##   `Phone Number` = col_character(),
##   Timezone = col_character(),
##   Longitude = col_double(),
##   Latitude = col_double()
## )

starbucks_us_by_state <- Starbucks %>% 
  filter(Country == "US") %>% 
  count(`State/Province`) %>% 
  mutate(state_name = str_to_lower(abbr2state(`State/Province`))) 

# Lisa's favorite St. Paul places - example for you to create your own data
favorite_stp_by_lisa <- tibble(
  place = c("Home", "Macalester College", "Adams Spanish Immersion", 
            "Spirit Gymnastics", "Bama & Bapa", "Now Bikes",
            "Dance Spectrum", "Pizza Luce", "Brunson's"),
  long = c(-93.1405743, -93.1712321, -93.1451796, 
           -93.1650563, -93.1542883, -93.1696608, 
           -93.1393172, -93.1524256, -93.0753863),
  lat = c(44.950576, 44.9378965, 44.9237914,
          44.9654609, 44.9295072, 44.9436813, 
          44.9399922, 44.9468848, 44.9700727)
  )

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   state = col_character(),
##   fips = col_character(),
##   cases = col_double(),
##   deaths = col_double()
## )

#US states map information - coordinates used to draw borders
states_map <- map_data("state")

Put your homework on GitHub!

If you were not able to get set up on GitHub last week, go here and get set up first. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):

• Create a repository on GitHub, giving it a nice name so you know it is for the 4th weekly exercise assignment (follow the instructions in the document/video).
  
• Copy the repo name so you can clone it to your computer. In R Studio, go to file –> New project –> Version control –> Git and follow the instructions from the document/video.
  
• Download the code from this document and save it in the repository folder/project on your computer.
  
• In R Studio, you should then see the .Rmd file in the upper right corner in the Git tab (along with the .Rproj file and probably .gitignore).
  
• Check all the boxes of the files in the Git tab under Stage and choose commit.
  
• In the commit window, write a commit message, something like “Initial upload” would be appropriate, and commit the files.
  
• Either click the green up arrow in the commit window or close the commit window and click the green up arrow in the Git tab to push your changes to GitHub.
  
• Refresh your GitHub page (online) and make sure the new documents have been pushed out.
  
• Back in R Studio, knit the .Rmd file. When you do that, you should have two (as long as you didn’t make any changes to the .Rmd file, in which case you might have three) files show up in the Git tab - an .html file and an .md file. The .md file is something we haven’t seen before and is here because I included keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).
  
• As you work through your homework, save and commit often, push changes occasionally (maybe after you feel finished with an exercise?), and go check to see what the .md file looks like on GitHub.
  
• If you have issues, let me know! This is new to many of you and may not be intuitive at first. But, I promise, you’ll get the hang of it!

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Warm-up exercises from tutorial

These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

Starbucks locations (ggmap)

1. Add the Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?

# Get the map information
world <- get_stamenmap(
    bbox = c(left = -180, bottom = -57, right = 179, top = 82.1), 
    maptype = "terrain",
    zoom = 2)

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# Plot the points on the map
ggmap(world) + # creates the map "background"
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude, color = `Ownership Type`),
             alpha = .5, 
             size = .6) +
  scale_color_viridis_d("viridis") +
  theme_map() + 
  theme(legend.background = element_blank())

## Warning: Removed 1 rows containing missing values (geom_point).

All the Starbucks in the United States appear to be either company owned or Licensed. The joint ventures appear to be limited to Europe and Asia. The Franchiese appear only in western Europe.

2. Construct a new map of Starbucks locations in the Twin Cities metro area (approximately the 5 county metro area).

# Get the map information
twin_cities_starbucks<- get_stamenmap(
    bbox = c(left = -93.75, bottom = 44.66, right = -92.42, top = 45.29), 
    maptype = "terrain",
    zoom = 10)

## Source : http://tile.stamen.com/terrain/10/245/367.png

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# Plot the points on the map
ggmap(twin_cities_starbucks) + # creates the map "background"
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude), 
             #alpha = .3, 
             size = .6) +
  theme_map() +
  theme(legend.background = element_blank()) 

## Warning: Removed 25451 rows containing missing values (geom_point).

3. In the Twin Cities plot, play with the zoom number. What does it do? (just describe what it does - don’t actually include more than one map).

Bigger numbers of zoom show more detail and are meant to be used with smaller areas. If you increase the zoom without changing the dimensions of the box the map almost appears smaller.

4. Try a couple different map types (see get_stamenmap() in help and look at maptype). Include a map with one of the other map types.

# Get the map information
twin_cities_starbucks<- get_stamenmap(
    bbox = c(left = -93.75, bottom = 44.66, right = -92.42, top = 45.29), 
    maptype = "watercolor",
    zoom = 10)

## Source : http://tile.stamen.com/watercolor/10/245/367.jpg

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# Plot the points on the map
ggmap(twin_cities_starbucks) + # creates the map "background"
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude), 
             #alpha = .3, 
             size = .6) +
  theme_map() +
  theme(legend.background = element_blank())

## Warning: Removed 25451 rows containing missing values (geom_point).

5. Add a point to the map that indicates Macalester College and label it appropriately. There are many ways you can do think, but I think it’s easiest with the annotate() function (see ggplot2 cheatsheet).

#why is the point not blue?

# Get the map information
twin_cities_starbucks<- get_stamenmap(
    bbox = c(left = -93.75, bottom = 44.66, right = -92.42, top = 45.29), 
    maptype = "terrain",
    zoom = 10)

# Plot the points on the map
ggmap(twin_cities_starbucks) + # creates the map "background"
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude), 
             #alpha = .3, 
             size = .6) +
  theme_map() +
  theme(legend.background = element_blank()) + 
  annotate(geom = "text", label = "Macalester College", y = 44.9382502, x = -93.170278, color = "orange") +
  annotate(geom = "point",y = 44.9382502, x = -93.170278, color = "blue" )

## Warning: Removed 25451 rows containing missing values (geom_point).

Choropleth maps with Starbucks data (geom_map())

The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% #Renames this whole code chunk to be located with the name census_pop_est_2018. It also reads in the csv file with the data from census. 
  separate(state, into = c("dot","state"), extra = "merge") %>% #Separates the dot and the state name, the extra part of the function makes sure that states with two words don't get separated into seperate rows (ex. north dakota)
  select(-dot) %>% #Gets rid of the dot which had been separated into its own column
  mutate(state = str_to_lower(state)) #puts all the states into lowercase letters

## Parsed with column specification:
## cols(
##   state = col_character(),
##   est_pop_2018 = col_double()
## )

starbucks_with_2018_pop_est <- #Allows this code chunk to be accessible with "starbucks_with_2018_pop_est
  starbucks_us_by_state %>% #Reads in the Starbucks data 
  left_join(census_pop_est_2018, 
            by = c("state_name" = "state")) %>% #Joins the starbucks data with the Census data from 2018 and joins them by the state names
  mutate(starbucks_per_10000 = (n/est_pop_2018)*10000) #Finds the proportion of starbucks per 10,000 people

6. dplyr review: Look through the code above and describe what each line of code does.

Code above is now annotated.

7. Create a choropleth map that shows the number of Starbucks per 10,000 people on a map of the US. Use a new fill color, add points for all Starbucks in the US (except Hawaii and Alaska), add an informative title for the plot, and include a caption that says who created the plot (you!). Make a conclusion about what you observe.

starbucks_without_hawaii_alaska <- Starbucks %>% 
  filter(`Country` == "US", 
         !`State/Province` %in% c("AK", "HI")) 
starbucks_without_hawaii_alaska

starbucks_with_2018_pop_est %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state_name,
               fill = n)) +
  geom_point(data = starbucks_without_hawaii_alaska,
             aes(x = Longitude, y = Latitude),
             size = .05,
             alpha = .2, 
             color = "goldenrod") +
  scale_fill_viridis_c() + 
  expand_limits(x = states_map$long, y = states_map$lat) + 
  labs(title = "Starbucks in the Continental United States", caption = "Made by Emma Iverson") + 
  theme_map() +
  theme(legend.background = element_blank())

It appears that although there are thousands of Starbucks throughout the United States that California has the most amount of Starbucks per 10,000 people.

A few of your favorite things (leaflet)

8. In this exercise, you are going to create a single map of some of your favorite places! The end result will be one map that satisfies the criteria below.

• Create a data set using the tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.

• Create a leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.

• Connect all your locations together with a line in a meaningful way (you may need to order them differently in the original data).

• If there are other variables you want to add that could enhance your plot, do that now.

#come back and reorder in a meanful way

# Emma's favorite places in the world - used in leaflet example
favorite_places_by_Emma <- tibble(
  place = c("Home", "Green Bay","Macalester College", 
            "First Host Family Home", "Prague", "Tower of London",
            "Cafe Latte", "Amsterdam", "Luverne", "Second Host Family Home", "First Host Family Beach Home","El Centro de Cartagena", "Brussels"),
  long = c(-93.1757177, -88.0539507, -93.1712321, 
           -1.0372262, 14.3255406, -0.078138, 
           -93.1627008, 4.90319, -96.2140906, -0.974951, 
           -0.7220011, -0.9853798, 4.3524774),
  lat = c(44.4602664, 44.5034043, 44.9378965, 
          37.615673, 50.0595854, 51.5081124, 
          44.9314816, 52.3659356, 43.6598514, 37.608771, 
          37.6605627, 37.5987359, 50.8459514))

pal <- colorFactor("orange", 
                     domain = favorite_places_by_Emma$place, levels = c("Home", 
                                                                        "Green Bay", "El Centro de Cartagena"), na.color = "purple" ) 

leaflet(data = favorite_places_by_Emma) %>% 
  addTiles() %>% 
  addCircles(data = favorite_places_by_Emma, 
             lng = ~long, 
             lat = ~lat, 
             weight = 10, 
             opacity = 1, 
             label = ~place, 
             color = ~pal(place)) %>%
  addLegend(pal = pal, 
            values = ~place, 
            opacity = 0.5, 
            title = "My Favorite Places in the World; Orange Denotes Top 3",
            position = "bottomright") %>% 
  addPolylines(lng = ~long, 
                lat = ~lat, 
                color = "blue")

## Warning in pal(place): Some values were outside the color scale and will be
## treated as NA

## Warning in pal(place): Some values were outside the color scale and will be
## treated as NA

## Warning in pal(v): Some values were outside the color scale and will be treated
## as NA

Revisiting old datasets

This section will revisit some datasets we have used previously and bring in a mapping component.

Bicycle-Use Patterns

The data come from Washington, DC and cover the last quarter of 2014.

Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations <- read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

## Parsed with column specification:
## cols(
##   name = col_character(),
##   lat = col_double(),
##   long = col_double(),
##   nbBikes = col_double(),
##   nbEmptyDocks = col_double()
## )

9. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.

total_departures_withlatlong <- Trips %>%
  left_join(Stations,
            by = c("sstation" = "name")) %>% 
  group_by(sstation) %>% 
  distinct(sstation, tot_dep = n(), lat, long)

color_pal <- colorNumeric("viridis", domain = total_departures_withlatlong$tot_dep)

leaflet(data = total_departures_withlatlong) %>% 
  addProviderTiles(providers$CartoDB.DarkMatter) %>% 
  addCircles(lng = ~long, 
             lat = ~lat,
             label = ~sstation, 
             weight = 10, 
             opacity = 1, 
             color = ~color_pal(tot_dep)) %>% 
  addLegend(pal = color_pal, 
            values = ~tot_dep,
            bins = 10,
            opacity = 0.5, 
            title = "Number of Depatures by Station", 
            position = "bottomright")

## Warning in validateCoords(lng, lat, funcName): Data contains 12 rows with either
## missing or invalid lat/lon values and will be ignored

10. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? Also plot this on top of a map. I think it will be more clear what the patterns are.

total_departures_casual <- Trips %>%
  left_join(Stations,
            by = c("sstation" = "name")) %>%  
  group_by(sstation, long, lat) %>% 
  summarize(percent_casual = mean(client == "Casual")) 

## `summarise()` regrouping output by 'sstation', 'long' (override with `.groups` argument)

color_pals <- colorNumeric("viridis", domain = total_departures_casual$percent_casual)
  

leaflet(data = total_departures_casual) %>% 
  addProviderTiles(providers$CartoDB.DarkMatter) %>% 
  addCircles(lng = ~long, 
             lat = ~lat,
             label = ~sstation, 
             weight = 10, 
             opacity = 1, 
             color = ~color_pals(percent_casual)) %>% 
  addLegend(pal = color_pals, 
            values = ~percent_casual,
            bins = 10,
            opacity = 0.5, 
            title = "Percent Depatures by Casual users per Station", 
            position = "bottomright")

## Warning in validateCoords(lng, lat, funcName): Data contains 12 rows with either
## missing or invalid lat/lon values and will be ignored

The biggest percent departures by causal users are in the tourist hot spots of DC. Near the mall and monuments. So the pattern for casual users (aka not commuters) is to use the bicycles in the tourist areas.

COVID-19 data

The following exercises will use the COVID-19 data from the NYT.

11. Create a map that colors the states by the most recent cumulative number of COVID-19 cases (remember, these data report cumulative numbers so you don’t need to compute that). Describe what you see. What is the problem with this map?

lower_case_nam <- covid19 %>% 
  mutate(state_nam = str_to_lower(`state`)) %>% 
  group_by(state_nam) %>% 
  summarise(recent_case_count = (max(cases))) %>% 
  arrange(recent_case_count)

## `summarise()` ungrouping output (override with `.groups` argument)

lower_case_nam %>% 
  ggplot() + 
  geom_map(map = states_map, 
           aes(map_id = state_nam, 
               fill =  recent_case_count)) + 
  scale_fill_viridis_c() + 
  expand_limits(x = states_map$long, y = states_map$lat) +
  labs(title = "Cumulative Number of Covid-19 Cases Per State", caption = "Made by Emma Iverson") + 
  theme_map() +
  theme(legend.background = element_blank())

The Problem with this map is that it does not take into account the population of each state. So California, Texas, New York, and Florida have a higher cumulative case count than other states, but they also have much higher populations than say North Dakota. Without taking into account the population of each state it does not accurately depict the rate of COVID-19 in each state.

12. Now add the population of each state to the dataset and color the states by most recent cumulative cases/10,000 people. See the code for doing this with the Starbucks data. You will need to make some modifications.

covid_with_populations <- covid19 %>%
  mutate(state_nam = str_to_lower(`state`)) %>% 
  group_by(state_nam) %>% 
  summarize(total_cases = max(cases)) %>% 
  left_join(census_pop_est_2018,
            by = c("state_nam" = "state")) %>% 
  mutate(covid19_per_state_per_10000 = (total_cases/est_pop_2018)*10000) %>% 
  ggplot(aes(fill = covid19_per_state_per_10000)) + 
  geom_map(map = states_map, 
           aes(map_id = state_nam)) + 
  scale_fill_viridis_c() + 
  expand_limits(x = states_map$long, y = states_map$lat) +
  labs(title = "Cumulative Cases per 10,000 people of Covid-19 Per State", caption = "Made by Emma Iverson") + 
  theme_map() +
  theme(legend.background = element_blank(), legend.position = "top")

## `summarise()` ungrouping output (override with `.groups` argument)

covid_with_populations

#get help

13. CHALLENGE Choose 4 dates spread over the time period of the data and create the same map as in exercise 12 for each of the dates. Display the four graphs together using faceting. What do you notice?

#all of the dates
covid19%>% 
  mutate(state_nam = str_to_lower(`state`), 
         r_cases = replace_na(cases, 0)) %>%
  filter(date == "2020-03-20" | date == "2020-05-20" |
           date == "2020-07-20" | date == "2020-09-20" ) %>%
  group_by(state_nam) %>% 
  left_join(census_pop_est_2018,
            by = c("state_nam" = "state")) %>% 
  mutate(covid19_per_state_per_10000_date = (r_cases/est_pop_2018)*10000)%>%
  ggplot() + 
  geom_map(map = states_map, 
           aes(map_id = state_nam, 
               fill =  covid19_per_state_per_10000_date)) + 
  scale_fill_viridis_c() + 
  expand_limits(x = states_map$long, y = states_map$lat) +
  labs(title = "Cumulative Cases per 10,000 people of Covid-19 Per State", caption = "Made by Emma Iverson") + 
  theme_map() +
  theme(legend.background = element_blank(), legend.position = "left") + 
  facet_wrap(vars(date))

The further into the pandemic we have gotten the cases per 10,000 have occurred (duh). But you can see for example New York state has had issues with the proportion of COVID19 cases to 10,000 people since May. And slowly other states’ proportions have gotten worse. For example Louisiana’s has been bad since july but seems to have sky rocketed in September.

Minneapolis police stops

These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.

14. Use the MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.

mpls_suspicious <- MplsStops %>% 
  mutate(susp = problem == "suspicious") %>% 
  group_by(neighborhood) %>% 
  summarize(total_stops = n(), 
            prop_suspicious = sum(susp)/total_stops) %>% 
  arrange(desc(total_stops))

## `summarise()` ungrouping output (override with `.groups` argument)

mpls_suspicious

15. Use a leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.

color_sch <- colorFactor("viridis", domain = MplsStops$problem)

leaflet(data = MplsStops) %>% 
  addTiles() %>% 
  addCircleMarkers(lng = ~long, 
                   lat = ~lat, 
                   label = ~problem, 
                   weight = 10, 
                   opacity = 1, 
                   stroke = FALSE, 
                   color = ~color_sch(problem)) %>% 
   addLegend(pal = color_sch, 
            values = ~problem,
            bins = 10,
            opacity = 0.5, 
            title = "Each Stop made by the Minneapolis Police Department for the year 2017 Colored by Reason For the Stop ", 
            position = "bottomright")

## Warning in addLegend(., pal = color_sch, values = ~problem, bins = 10, opacity =
## 0.5, : 'bins' is ignored because the palette type is not numeric

16. Save the folder from moodle called Minneapolis_Neighborhoods into your project/repository folder for this assignment. Make sure the folder is called Minneapolis_Neighborhoods. Use the code below to read in the data and make sure to delete the eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.

mpls_nbhd <- st_read("Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE)

mplsDemSus <- mpls_suspicious %>% 
  left_join(MplsDemo, by = "neighborhood")

mpls_all <- mpls_nbhd %>% 
  left_join(mplsDemSus, by = c("BDNAME" = "neighborhood")) %>% 
  rename("neighborhood" = "BDNAME")

mpls_all

17. Use leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.

color_ti <- colorNumeric("viridis", domain = mpls_all$prop_suspicious)

leaflet(data = mpls_all) %>% 
  addTiles() %>% 
  addPolygons(stroke = FALSE,
              fillColor = ~color_ti(prop_suspicious),
              fillOpacity = 0.6,
              label = ~neighborhood)%>% 
   addLegend(pal = color_ti, 
            values = ~prop_suspicious,
            bins =10,
            opacity = 0.5, 
            title = " Proportion of stops made 
            by MPD for 'suspicious' activity by neighborhood", 
            position = "bottomleft")

The areas that have the largest proportion of stops do to ‘suspicious’ activity is just north of the MSP airport.

18. Use leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows.

Make a graph that shows average house hold income by neighborhood. Compare this graph to the previous problem’s graph. What do you notice? Is it what you expected?

color_income <- colorNumeric("viridis", domain = mpls_all$hhIncome)

leaflet(data = mpls_all) %>% 
  addTiles() %>% 
  addPolygons(stroke = FALSE,
              fillColor = ~color_income(hhIncome),
              fillOpacity = 0.6,
              label = ~neighborhood)%>% 
   addLegend(pal = color_income, 
            values = ~hhIncome,
            bins =10,
            opacity = 0.5, 
            title = "HH Income by neighborhood", 
            position = "bottomleft")

Based on past experiences with police data I expected the lowest house hold income neighborhoods to have the have the highest proportion of stops due to “suspicious.” The graph shows some of this but does not demonstrate this conclusively. The neighborhoods just north of the airport have a larger proportion of stops due to “suspicious” but these neighborhoods do not have the lowest average house hold income. They are not high income neighborhoods but they are not the lowest. That being said the lowest house hold income neighborhoods do have a high proportion (upwards of 0.6) of stops due to “suspicious.”

GitHub link

19. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 04_exercises.Rmd, provide a link to the 04_exercises.md file, which is the one that will be most readable on GitHub.

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?